Overview

Dataset statistics

Number of variables13
Number of observations950
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.6 KiB
Average record size in memory104.1 B

Variable types

Categorical1
Numeric12

Alerts

amperestunden is highly correlated with zyklus_ and 5 other fieldsHigh correlation
zyklus_ is highly correlated with amperestunden and 4 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 3 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 3 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 3 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 6 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with amperestunden and 6 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 4 other fieldsHigh correlation
time_pause_vorher is highly correlated with amperestunden and 5 other fieldsHigh correlation
time_temp_hoch is highly correlated with temperature_amax and 3 other fieldsHigh correlation
time_temp_hoch_vorher is highly correlated with temperature_amax and 7 other fieldsHigh correlation
amperestunden is highly correlated with zyklus_ and 4 other fieldsHigh correlation
zyklus_ is highly correlated with amperestunden and 2 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 1 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 1 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 1 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 5 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with amperestunden and 2 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_pause_vorher is highly correlated with time_amin and 2 other fieldsHigh correlation
time_temp_hoch is highly correlated with time_temp_hoch_vorherHigh correlation
time_temp_hoch_vorher is highly correlated with time_temp_hochHigh correlation
amperestunden is highly correlated with zyklus_ and 4 other fieldsHigh correlation
zyklus_ is highly correlated with amperestunden and 3 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 2 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 2 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 2 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 4 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with amperestunden and 4 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with amperestunden and 2 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_pause_vorher is highly correlated with time_amin and 1 other fieldsHigh correlation
time_temp_hoch is highly correlated with temperature_amax and 2 other fieldsHigh correlation
batteryname_ is highly correlated with amperestunden and 9 other fieldsHigh correlation
amperestunden is highly correlated with batteryname_ and 7 other fieldsHigh correlation
zyklus_ is highly correlated with batteryname_ and 8 other fieldsHigh correlation
temperature_amax is highly correlated with batteryname_ and 2 other fieldsHigh correlation
temperature_amin is highly correlated with batteryname_ and 3 other fieldsHigh correlation
temperature_mean is highly correlated with batteryname_ and 2 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 7 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with batteryname_ and 8 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with batteryname_ and 8 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 6 other fieldsHigh correlation
time_pause_vorher is highly correlated with batteryname_ and 6 other fieldsHigh correlation
time_temp_hoch is highly correlated with batteryname_ and 6 other fieldsHigh correlation
time_temp_hoch_vorher is highly correlated with batteryname_ and 9 other fieldsHigh correlation
time_amin has unique values Unique
time_entladen_leicht_vorher has unique values Unique
time_laden_stark_vorher has unique values Unique
time_entladen_stark_vorher has 40 (4.2%) zeros Zeros
time_pause_vorher has 22 (2.3%) zeros Zeros
time_temp_hoch has 593 (62.4%) zeros Zeros
time_temp_hoch_vorher has 33 (3.5%) zeros Zeros

Reproduction

Analysis started2021-12-12 19:01:15.368392
Analysis finished2021-12-12 19:01:33.757732
Duration18.39 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

batteryname_
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
RW9
80 
RW11
77 
RW10
77 
RW12
76 
RW2
 
52
Other values (23)
588 

Length

Max length4
Median length4
Mean length3.554736842
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRW1
2nd rowRW1
3rd rowRW1
4th rowRW1
5th rowRW1

Common Values

ValueCountFrequency (%)
RW980
 
8.4%
RW1177
 
8.1%
RW1077
 
8.1%
RW1276
 
8.0%
RW252
 
5.5%
RW749
 
5.2%
RW848
 
5.1%
RW148
 
5.1%
RW344
 
4.6%
RW542
 
4.4%
Other values (18)357
37.6%

Length

2021-12-12T20:01:33.831506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rw980
 
8.4%
rw1177
 
8.1%
rw1077
 
8.1%
rw1276
 
8.0%
rw252
 
5.5%
rw749
 
5.2%
rw848
 
5.1%
rw148
 
5.1%
rw344
 
4.6%
rw542
 
4.4%
Other values (18)357
37.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amperestunden
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct948
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5014888
Minimum0
Maximum2.147192722
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:33.930843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.011283349
Q11.253411514
median1.491576156
Q31.754460949
95-th percentile2.043836863
Maximum2.147192722
Range2.147192722
Interquartile range (IQR)0.5010494347

Descriptive statistics

Standard deviation0.3325745011
Coefficient of variation (CV)0.2214964914
Kurtosis0.2232667832
Mean1.5014888
Median Absolute Deviation (MAD)0.2526541546
Skewness-0.2003476861
Sum1426.41436
Variance0.1106057988
MonotonicityNot monotonic
2021-12-12T20:01:34.044614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.3%
1.7819399091
 
0.1%
1.7467561541
 
0.1%
2.0089837571
 
0.1%
2.0029339881
 
0.1%
1.9434059371
 
0.1%
1.943111551
 
0.1%
1.8632531711
 
0.1%
1.864670051
 
0.1%
1.8012449121
 
0.1%
Other values (938)938
98.7%
ValueCountFrequency (%)
03
0.3%
0.69297840441
 
0.1%
0.74877821141
 
0.1%
0.7502029931
 
0.1%
0.7587395321
 
0.1%
0.75977552551
 
0.1%
0.77836437461
 
0.1%
0.78493920361
 
0.1%
0.81164762651
 
0.1%
0.81683376491
 
0.1%
ValueCountFrequency (%)
2.1471927221
0.1%
2.1415824041
0.1%
2.1388317841
0.1%
2.1374373551
0.1%
2.1369088381
0.1%
2.1348230391
0.1%
2.1346482831
0.1%
2.1339161521
0.1%
2.1326148161
0.1%
2.1319957931
0.1%

zyklus_
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28218.97053
Minimum1
Maximum113576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:34.139769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile865.8
Q17952.75
median16073
Q339511.25
95-th percentile93463.65
Maximum113576
Range113575
Interquartile range (IQR)31558.5

Descriptive statistics

Standard deviation28321.90788
Coefficient of variation (CV)1.003647807
Kurtosis0.8231802114
Mean28218.97053
Median Absolute Deviation (MAD)11970.5
Skewness1.327962722
Sum26808022
Variance802130466
MonotonicityNot monotonic
2021-12-12T20:01:34.249133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219
 
2.0%
38
 
0.8%
55
 
0.5%
14
 
0.4%
242094
 
0.4%
30314
 
0.4%
30334
 
0.4%
60574
 
0.4%
90874
 
0.4%
121114
 
0.4%
Other values (851)890
93.7%
ValueCountFrequency (%)
14
 
0.4%
219
2.0%
38
0.8%
43
 
0.3%
55
 
0.5%
91
 
0.1%
401
 
0.1%
472
 
0.2%
8541
 
0.1%
8561
 
0.1%
ValueCountFrequency (%)
1135761
0.1%
1135721
0.1%
1108171
0.1%
1105551
0.1%
1105511
0.1%
1093871
0.1%
1091991
0.1%
1091951
0.1%
1078001
0.1%
1077961
0.1%

temperature_amax
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct810
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-343.1336119
Minimum-4099.44775
Maximum44.93351
Zeros0
Zeros (%)0.0%
Negative90
Negative (%)9.5%
Memory size7.5 KiB
2021-12-12T20:01:34.358497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile-4093.927
Q124.64716
median27.059845
Q334.33386
95-th percentile43.01202
Maximum44.93351
Range4144.38126
Interquartile range (IQR)9.6867

Descriptive statistics

Standard deviation1183.820648
Coefficient of variation (CV)-3.450028231
Kurtosis6.184014818
Mean-343.1336119
Median Absolute Deviation (MAD)3.35148
Skewness-2.858384352
Sum-325976.9313
Variance1401431.326
MonotonicityNot monotonic
2021-12-12T20:01:34.452238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92748
 
5.1%
-4094.0981434
 
3.6%
24.435814
 
0.4%
24.337483
 
0.3%
24.461823
 
0.3%
23.673163
 
0.3%
24.26463
 
0.3%
27.257823
 
0.3%
23.315183
 
0.3%
43.093192
 
0.2%
Other values (800)844
88.8%
ValueCountFrequency (%)
-4099.447752
 
0.2%
-4094.0981434
3.6%
-4093.92748
5.1%
-4059.53762
 
0.2%
-70.10111
 
0.1%
-63.095591
 
0.1%
-57.931511
 
0.1%
-54.885531
 
0.1%
22.080741
 
0.1%
22.647441
 
0.1%
ValueCountFrequency (%)
44.933511
0.1%
44.771311
0.1%
44.750111
0.1%
44.666741
0.1%
44.433381
0.1%
44.326771
0.1%
44.278151
0.1%
44.099661
0.1%
44.08311
0.1%
44.077641
0.1%

temperature_amin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct807
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-369.2755797
Minimum-4099.44775
Maximum40.65417
Zeros0
Zeros (%)0.0%
Negative91
Negative (%)9.6%
Memory size7.5 KiB
2021-12-12T20:01:34.567882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile-4093.927
Q119.4810675
median21.80882
Q328.5679825
95-th percentile38.9023835
Maximum40.65417
Range4140.10192
Interquartile range (IQR)9.086915

Descriptive statistics

Standard deviation1212.788638
Coefficient of variation (CV)-3.284237314
Kurtosis5.580768786
Mean-369.2755797
Median Absolute Deviation (MAD)3.336185
Skewness-2.751126451
Sum-350811.8007
Variance1470856.28
MonotonicityNot monotonic
2021-12-12T20:01:34.661596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92752
 
5.5%
-4094.0981434
 
3.6%
-4099.447753
 
0.3%
28.662853
 
0.3%
21.067483
 
0.3%
19.52993
 
0.3%
20.989653
 
0.3%
19.718283
 
0.3%
19.166853
 
0.3%
19.025372
 
0.2%
Other values (797)841
88.5%
ValueCountFrequency (%)
-4099.447753
 
0.3%
-4094.0981434
3.6%
-4093.92752
5.5%
-4059.53762
 
0.2%
16.498121
 
0.1%
17.16611
 
0.1%
17.383541
 
0.1%
17.50441
 
0.1%
17.532011
 
0.1%
17.555591
 
0.1%
ValueCountFrequency (%)
40.654171
0.1%
40.641241
0.1%
40.563911
0.1%
40.562791
0.1%
40.410311
0.1%
40.321741
0.1%
40.242421
0.1%
40.213081
0.1%
40.119251
0.1%
39.87411
0.1%

temperature_mean
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct870
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-362.7266643
Minimum-4099.44775
Maximum42.14114416
Zeros0
Zeros (%)0.0%
Negative91
Negative (%)9.6%
Memory size7.5 KiB
2021-12-12T20:01:34.771153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile-4093.927
Q122.3161411
median24.4982451
Q331.68772172
95-th percentile41.02405727
Maximum42.14114416
Range4141.588894
Interquartile range (IQR)9.371580623

Descriptive statistics

Standard deviation1206.597105
Coefficient of variation (CV)-3.326463765
Kurtosis5.690477797
Mean-362.7266643
Median Absolute Deviation (MAD)3.192067399
Skewness-2.770344446
Sum-344590.331
Variance1455876.574
MonotonicityNot monotonic
2021-12-12T20:01:34.865076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92739
 
4.1%
-4094.0981429
 
3.1%
-4093.9279
 
0.9%
-4094.098145
 
0.5%
-4059.53762
 
0.2%
-4099.447752
 
0.2%
23.911831811
 
0.1%
41.207987251
 
0.1%
41.016482791
 
0.1%
41.503607341
 
0.1%
Other values (860)860
90.5%
ValueCountFrequency (%)
-4099.447752
 
0.2%
-4094.098145
 
0.5%
-4094.0981429
3.1%
-4093.9279
 
0.9%
-4093.92739
4.1%
-4086.8160111
 
0.1%
-4059.53762
 
0.2%
-4036.990361
 
0.1%
-4004.9628011
 
0.1%
-3925.2898771
 
0.1%
ValueCountFrequency (%)
42.141144161
0.1%
42.117619871
0.1%
42.03270261
0.1%
41.916006231
0.1%
41.823378261
0.1%
41.816040311
0.1%
41.808441051
0.1%
41.754365611
0.1%
41.730017961
0.1%
41.697681481
0.1%

time_amin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6860446.252
Minimum1633.73
Maximum17577173.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:34.961823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1633.73
5-th percentile444543.7585
Q13126158.735
median6566721.22
Q310263763.56
95-th percentile14223182.87
Maximum17577173.69
Range17575539.96
Interquartile range (IQR)7137604.822

Descriptive statistics

Standard deviation4348495.994
Coefficient of variation (CV)0.6338503115
Kurtosis-0.8259038793
Mean6860446.252
Median Absolute Deviation (MAD)3508038.04
Skewness0.2879996498
Sum6517423939
Variance1.890941741 × 1013
MonotonicityNot monotonic
2021-12-12T20:01:35.243042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
385232.921
 
0.1%
3171585.871
 
0.1%
7858868.921
 
0.1%
208842.461
 
0.1%
226891.061
 
0.1%
814826.121
 
0.1%
832841.171
 
0.1%
1405694.421
 
0.1%
1424061.951
 
0.1%
2006333.651
 
0.1%
Other values (940)940
98.9%
ValueCountFrequency (%)
1633.731
0.1%
6294.221
0.1%
9754.781
0.1%
9800.311
0.1%
9800.841
0.1%
9813.111
0.1%
9849.621
0.1%
9966.341
0.1%
10018.011
0.1%
10092.751
0.1%
ValueCountFrequency (%)
17577173.691
0.1%
17485573.051
0.1%
17470024.951
0.1%
17298505.871
0.1%
17085565.631
0.1%
17083278.631
0.1%
17054456.081
0.1%
16966176.511
0.1%
16941806.021
0.1%
16873033.811
0.1%

time_entladen_stark_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct597
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205013.9202
Minimum0
Maximum593181.15
Zeros40
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:35.352411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22304.7675
Q1123008.6775
median203374.52
Q3264311.3625
95-th percentile480312.196
Maximum593181.15
Range593181.15
Interquartile range (IQR)141302.685

Descriptive statistics

Standard deviation123505.3834
Coefficient of variation (CV)0.602424378
Kurtosis0.8246641979
Mean205013.9202
Median Absolute Deviation (MAD)69319.5
Skewness0.7630255465
Sum194763224.2
Variance1.525357972 × 1010
MonotonicityNot monotonic
2021-12-12T20:01:35.446166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040
 
4.2%
239704.044
 
0.4%
201767.842
 
0.2%
45712.612
 
0.2%
26248.562
 
0.2%
228812.912
 
0.2%
226026.612
 
0.2%
221762.482
 
0.2%
219154.112
 
0.2%
214002.422
 
0.2%
Other values (587)890
93.7%
ValueCountFrequency (%)
040
4.2%
1441.611
 
0.1%
1597.061
 
0.1%
1616.741
 
0.1%
17749.622
 
0.2%
19338.981
 
0.1%
21926.612
 
0.2%
22766.962
 
0.2%
23109.021
 
0.1%
23298.041
 
0.1%
ValueCountFrequency (%)
593181.151
0.1%
590747.871
0.1%
588001.681
0.1%
584509.31
0.1%
581060.041
0.1%
577898.061
0.1%
574226.151
0.1%
570478.331
0.1%
566288.941
0.1%
562124.131
0.1%

time_entladen_leicht_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1358084.162
Minimum7538.12
Maximum4347433.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:35.572417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7538.12
5-th percentile123876.6955
Q1468996.755
median1105874.61
Q31896081.998
95-th percentile3784764.458
Maximum4347433.06
Range4339894.94
Interquartile range (IQR)1427085.243

Descriptive statistics

Standard deviation1094643.005
Coefficient of variation (CV)0.8060200065
Kurtosis0.1013639948
Mean1358084.162
Median Absolute Deviation (MAD)679998.72
Skewness0.9663618627
Sum1290179954
Variance1.198243308 × 1012
MonotonicityNot monotonic
2021-12-12T20:01:35.684206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196178.51
 
0.1%
714809.151
 
0.1%
337253.441
 
0.1%
198416.221
 
0.1%
205627.71
 
0.1%
318912.521
 
0.1%
325908.511
 
0.1%
420498.371
 
0.1%
427212.081
 
0.1%
517643.841
 
0.1%
Other values (940)940
98.9%
ValueCountFrequency (%)
7538.121
0.1%
7539.591
0.1%
7542.951
0.1%
7552.61
0.1%
7610.031
0.1%
7616.331
0.1%
7629.051
0.1%
7634.981
0.1%
7661.041
0.1%
7665.271
0.1%
ValueCountFrequency (%)
4347433.061
0.1%
4343519.611
0.1%
4268724.251
0.1%
4264733.781
0.1%
4234224.361
0.1%
4200003.531
0.1%
4184108.451
0.1%
4179980.681
0.1%
4159345.361
0.1%
41559061
0.1%

time_laden_stark_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3649026.058
Minimum1627.08
Maximum10850409.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:35.777972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1627.08
5-th percentile180774.203
Q11667120.555
median3387895.06
Q35026416.302
95-th percentile8405080.343
Maximum10850409.36
Range10848782.28
Interquartile range (IQR)3359295.747

Descriptive statistics

Standard deviation2470853.712
Coefficient of variation (CV)0.6771269025
Kurtosis-0.3779777288
Mean3649026.058
Median Absolute Deviation (MAD)1671319.635
Skewness0.5570831566
Sum3466574755
Variance6.105118064 × 1012
MonotonicityNot monotonic
2021-12-12T20:01:35.887315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194718.281
 
0.1%
2211298.911
 
0.1%
4734429.791
 
0.1%
11155.931
 
0.1%
21971.541
 
0.1%
444310.171
 
0.1%
455328.221
 
0.1%
881343.111
 
0.1%
893002.11
 
0.1%
1333416.551
 
0.1%
Other values (940)940
98.9%
ValueCountFrequency (%)
1627.081
0.1%
6289.181
0.1%
9394.741
0.1%
9440.271
0.1%
9440.81
0.1%
9453.071
0.1%
9489.581
0.1%
9606.31
0.1%
9657.971
0.1%
9732.741
0.1%
ValueCountFrequency (%)
10850409.361
0.1%
10769780.971
0.1%
10468424.331
0.1%
10381041.461
0.1%
10306316.741
0.1%
10086358.661
0.1%
9995981.551
0.1%
9910653.221
0.1%
9763684.431
0.1%
9702559.451
0.1%

time_pause_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct798
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1050077.314
Minimum0
Maximum5585429.23
Zeros22
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:35.996697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16047.58
Q1129139.4
median648877.985
Q31504034.133
95-th percentile3759876.513
Maximum5585429.23
Range5585429.23
Interquartile range (IQR)1374894.733

Descriptive statistics

Standard deviation1183726.529
Coefficient of variation (CV)1.127275595
Kurtosis2.110042012
Mean1050077.314
Median Absolute Deviation (MAD)555902.795
Skewness1.575044181
Sum997573448.2
Variance1.401208495 × 1012
MonotonicityNot monotonic
2021-12-12T20:01:36.090422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022
 
2.3%
30016
 
1.7%
14570.712
 
0.2%
16048.392
 
0.2%
16047.582
 
0.2%
28480.92
 
0.2%
43308.442
 
0.2%
54539.12
 
0.2%
68173.62
 
0.2%
79399.12
 
0.2%
Other values (788)896
94.3%
ValueCountFrequency (%)
022
2.3%
30016
1.7%
114001
 
0.1%
14570.712
 
0.2%
14574.912
 
0.2%
15817.742
 
0.2%
16046.882
 
0.2%
16047.582
 
0.2%
16048.392
 
0.2%
16065.262
 
0.2%
ValueCountFrequency (%)
5585429.231
0.1%
5470150.931
0.1%
5378402.951
0.1%
5338495.031
0.1%
5302217.341
0.1%
5261931.651
0.1%
5170176.721
0.1%
5130269.141
0.1%
5093991.631
0.1%
5053697.841
0.1%

time_temp_hoch
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct358
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1977.097947
Minimum0
Maximum7730.64
Zeros593
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:36.215446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34522.975
95-th percentile6904.285
Maximum7730.64
Range7730.64
Interquartile range (IQR)4522.975

Descriptive statistics

Standard deviation2664.734842
Coefficient of variation (CV)1.347801127
Kurtosis-1.11352209
Mean1977.097947
Median Absolute Deviation (MAD)0
Skewness0.764488618
Sum1878243.05
Variance7100811.776
MonotonicityNot monotonic
2021-12-12T20:01:36.324783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0593
62.4%
5552.061
 
0.1%
4684.591
 
0.1%
4439.531
 
0.1%
4436.761
 
0.1%
4376.341
 
0.1%
4364.011
 
0.1%
4495.31
 
0.1%
4513.651
 
0.1%
4784.881
 
0.1%
Other values (348)348
36.6%
ValueCountFrequency (%)
0593
62.4%
3001
 
0.1%
2695.91
 
0.1%
2700.841
 
0.1%
2731.811
 
0.1%
2735.551
 
0.1%
2802.391
 
0.1%
2826.011
 
0.1%
2940.841
 
0.1%
2967.321
 
0.1%
ValueCountFrequency (%)
7730.641
0.1%
7710.561
0.1%
7700.661
0.1%
7695.551
0.1%
7693.171
0.1%
7685.781
0.1%
7685.271
0.1%
7683.21
0.1%
7678.221
0.1%
7661.041
0.1%

time_temp_hoch_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct638
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2680278.352
Minimum0
Maximum11409748.64
Zeros33
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-12T20:01:36.449780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile139765.7
Q1793201.45
median1767303.76
Q33737098.882
95-th percentile8027307.819
Maximum11409748.64
Range11409748.64
Interquartile range (IQR)2943897.432

Descriptive statistics

Standard deviation2439237.488
Coefficient of variation (CV)0.9100687197
Kurtosis0.7340155128
Mean2680278.352
Median Absolute Deviation (MAD)1314359.26
Skewness1.205400973
Sum2546264435
Variance5.949879525 × 1012
MonotonicityNot monotonic
2021-12-12T20:01:36.562544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1578427.234
 
3.6%
033
 
3.5%
3739006.8214
 
1.5%
1341971.3812
 
1.3%
3393493.8510
 
1.1%
1497579.6310
 
1.1%
3038733.019
 
0.9%
1469602.179
 
0.9%
405011.468
 
0.8%
405611.668
 
0.8%
Other values (628)803
84.5%
ValueCountFrequency (%)
033
3.5%
17395.41
 
0.1%
17591.571
 
0.1%
17757.171
 
0.1%
17797.631
 
0.1%
18462.931
 
0.1%
18506.41
 
0.1%
18585.121
 
0.1%
21609.261
 
0.1%
35609.141
 
0.1%
ValueCountFrequency (%)
11409748.641
0.1%
10922820.591
0.1%
10890510.411
0.1%
10377187.111
0.1%
10346045.081
0.1%
10219232.411
0.1%
10186149.511
0.1%
9830754.111
0.1%
9798870.251
0.1%
9651188.081
0.1%

Interactions

2021-12-12T20:01:32.092518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:17.791845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.104216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.360023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.563567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.863058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.106709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.431346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.816999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.047977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.322605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.744354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.193345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:17.910721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.211819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.457463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.675621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.979461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.219957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.635315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.928214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.149619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.422388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.849247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.305423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.012993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.300686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.556520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.839608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.084396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.319817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.750778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.016614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.263950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.535305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.954294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.393385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.112589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.403670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.658417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.947209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.177895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.433759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.856863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.116251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.362466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.621383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.053645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.492130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.210282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.503362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.756556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.057058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.282163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.536865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.959520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.220321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.474974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.736944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.171631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.591740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.305286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.601270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.853478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.153893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.365120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.650518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.052945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.320819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.579090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.835282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.277852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.717767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.416166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.716650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.950907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.259594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.485511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.755064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.166831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.427556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.680707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.938023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.399191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.822457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.525389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.826317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.050217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.359697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.580373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.869176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.283765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.530955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.795827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.051411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.512613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:32.921594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.629350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.913618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.163179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.469877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.694771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.986279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.382617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.631034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:28.903693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.303328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.629802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:33.032208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.804681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.030463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.267812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.561057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.792031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.098343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.497264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.733868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.006502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.402547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.744808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:33.120037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:18.898371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.144095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.369233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.665650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:23.891073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.202842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.595582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.831964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.106385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.504672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.843449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:33.240276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:19.009997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:20.262749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:21.465435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:22.765029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:24.008296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:25.318559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:26.724997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:27.952629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:29.225794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:30.622713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-12T20:01:31.974436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-12T20:01:36.687664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-12T20:01:36.843892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-12T20:01:37.000575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-12T20:01:37.158616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-12T20:01:33.437539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-12T20:01:33.656843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

batteryname_amperestundenzyklus_temperature_amaxtemperature_amintemperature_meantime_amintime_entladen_stark_vorhertime_entladen_leicht_vorhertime_laden_stark_vorhertime_pause_vorhertime_temp_hochtime_temp_hoch_vorher
0RW12.000364323.5674218.3902521.798768385232.920.00196178.50194718.280.000.00.00
1RW12.000250524.0663918.1540321.449898405533.190.00203379.98207816.730.000.00.00
2RW11.958481100922.8189617.5713320.530506839519.2117749.62312669.57472156.6016048.390.0178418.56
3RW11.963192101123.6453818.1225321.420698858525.6217749.62319737.63484111.8616048.390.0178418.56
4RW11.911217192523.8948717.6658220.8394321248403.4736155.14406768.88743215.3328472.480.0344313.14
5RW11.915461192723.3491217.5555920.9339731267739.9536155.14413665.08755670.7928472.480.0344313.14
6RW11.869892282923.6453818.2485221.3556581655925.8153512.10507852.99991901.3543291.220.0508390.77
7RW11.866606283122.6474417.8548120.9669771675254.4753512.10514573.531004497.9743291.220.0508390.77
8RW11.826606378123.3179318.4060021.0186912135722.5771716.23607684.521325711.8755716.520.0716060.80
9RW11.832734378323.6142018.9257021.7026102154750.8071716.23614282.981338163.6055716.520.0716060.80

Last rows

batteryname_amperestundenzyklus_temperature_amaxtemperature_amintemperature_meantime_amintime_entladen_stark_vorhertime_entladen_leicht_vorhertime_laden_stark_vorhertime_pause_vorhertime_temp_hochtime_temp_hoch_vorher
940RW90.88583610150039.5280235.2150137.68045411671142.74311966.093883905.644709299.38964202.213189.268.559283e+06
941RW90.85918910150437.5956732.3242135.29166111699932.70311966.093886998.994727400.08971702.213093.358.587977e+06
942RW90.82418910451537.6884232.2005435.51837911926129.42314060.123952264.574823001.00994867.832967.328.775022e+06
943RW90.83732210451938.2449432.6952235.36495811956170.18314060.123955279.044842574.441002367.833014.478.805110e+06
944RW90.77836410753637.7038831.8140735.17433812177284.66315859.214018987.094929998.111029183.272802.398.985943e+06
945RW90.78493910754037.8893931.9686635.24565812207354.10315859.214021813.104949765.161036683.272826.019.016036e+06
946RW90.75977611055137.7966332.1077935.34340512427168.28317601.494080113.575040620.521059934.582735.559.190943e+06
947RW90.75874011055537.5338431.5976534.76850612457189.16317601.494082845.385060405.851067434.582731.819.220960e+06
948RW90.74877811357238.2913232.4015135.71939312672750.22319549.854142731.525144349.951094276.632695.909.394390e+06
949RW90.75020311357638.8632932.4633435.95851412703478.83319549.854145432.365164882.661101776.632700.849.425124e+06